Create README.md

README.md

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+---
+pipeline_tag: text-to-video
+---
+AnimateDiff is a method that allows you to create videos using pre-existing Stable Diffusion Text to Image models.
+
+Converted https://huggingface.co/guoyww/animatediff/blob/main/mm_sdxl_v10_beta.ckpt to Huggingface Diffusers format using following script based Diffuser's convetion script (available https://github.com/huggingface/diffusers/blob/main/scripts/convert_animatediff_motion_module_to_diffusers.py)
+
+```
+import argparse
+
+import torch
+
+from diffusers import MotionAdapter
+
+
+def convert_motion_module(original_state_dict):
+    converted_state_dict = {}
+    for k, v in original_state_dict.items():
+        if "pos_encoder" in k:
+            continue
+
+        else:
+            converted_state_dict[
+                k.replace(".norms.0", ".norm1")
+                .replace(".norms.1", ".norm2")
+                .replace(".ff_norm", ".norm3")
+                .replace(".attention_blocks.0", ".attn1")
+                .replace(".attention_blocks.1", ".attn2")
+                .replace(".temporal_transformer", "")
+            ] = v
+
+    return converted_state_dict
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ckpt_path", type=str, required=True)
+    parser.add_argument("--output_path", type=str, required=True)
+    parser.add_argument("--use_motion_mid_block", action="store_true")
+    parser.add_argument("--motion_max_seq_length", type=int, default=32)
+    parser.add_argument("--save_fp16", action="store_true")
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    state_dict = torch.load(args.ckpt_path, map_location="cpu")
+    if "state_dict" in state_dict.keys():
+        state_dict = state_dict["state_dict"]
+
+    conv_state_dict = convert_motion_module(state_dict)
+    adapter = MotionAdapter(
+        use_motion_mid_block=False,
+        motion_max_seq_length=32,
+        block_out_channels=(320, 640, 1280),
+    )
+    # skip loading position embeddings
+    adapter.load_state_dict(conv_state_dict, strict=False)
+    adapter.save_pretrained(args.output_path)
+
+    if args.save_fp16:
+        adapter.to(torch.float16).save_pretrained(args.output_path, variant="fp16")
+        
+```
+
+The following example demonstrates how you can utilize the motion modules with an existing Stable Diffusion text to image model.
+
+#TODO